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Record W2801774192 · doi:10.2196/mhealth.8832

Mobile-Based Nutrition Counseling and Unconditional Cash Transfers for Improving Maternal and Child Nutrition in Bangladesh: Pilot Study

2018· article· en· W2801774192 on OpenAlexvenueno aff
Tanvir Huda, Ashraful Alam, Tazeen Tahsina, Mohammad Mehedi Hasan, Jasmin Khan, Mohammad Masudur Rahman, Abu Bakkar Siddique, Shams El Arifeen, Michael J. Dibley

Bibliographic record

VenueJMIR mhealth and uhealth · 2018
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsychological interventionFocus groupmHealthMobile phoneIntervention (counseling)MalnutritionEnvironmental healthPopulationNutrition EducationPhoneFamily medicineGerontologyNursingBusinessMarketingTelecommunications

Abstract

fetched live from OpenAlex

BACKGROUND: Inappropriate feeding practices, inadequate nutrition knowledge, and insufficient access to food are major risk factors for maternal and child undernutrition. There is evidence to suggest that the combination of cash transfer and nutrition education improves child growth. However, a cost-effective delivery platform is needed to achieve complete, population-wide coverage of these interventions. OBJECTIVE: This study aimed to assess the feasibility, acceptability, and perceived appropriateness of an intervention package consisting of voice messaging, direct counseling, and unconditional cash transfers all on a mobile platform for changing perceptions on nutrition during pregnancy and the first year of a child's life in a poor rural community in Bangladesh. METHODS: We conducted a mixed-methods pilot study. We recruited 340 pregnant or recently delivered, lactating women from rural Bangladesh. The intervention consisted of an unconditional cash transfer combined with nutrition counseling, both delivered on a mobile platform. The participants received a mobile phone and BDT 787 per month (US $10). We used a voice messaging service to deliver nutrition-related messages. We provided additional nutrition counseling through a nutrition counselor from a call center. We carried out cross-sectional surveys at baseline and at the end of the study, focus group discussions, and in-depth interviews with participants and their family members. RESULTS: Approximately 89% (245/275) of participants reported that they were able to operate the mobile phones without much trouble. Charging of the mobile handsets posed some challenges since only approximately 45% (124/275) households in our study had electricity at home. Approximately 26% (72/275) women reported they had charged their mobile phones at their neighbor's house, while 34% (94/275) reported that they charged it at a marketplace. Less than 10% (22/275) of women reported difficulties understanding the voice messages or direct counseling through mobile phones, while only 3% (8/275) of women reported they had some problems withdrawing cash from the mobile bank agent. Approximately 87% (236/275) women reported spending the cash to purchase food for themselves and their children. CONCLUSIONS: The nature of our study precludes any conclusion about the effectiveness of the intervention package. However, the high coverage of our intervention and the positive feedback from the mothers were encouraging and support the feasibility, acceptability, and appropriateness of this program. Further research is needed to determine the efficacy and cost-effectiveness of mobile-based nutrition counseling and unconditional cash transfers in improving maternal and child nutrition in Bangladesh.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.341
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations39
Published2018
Admission routes1
Has abstractyes

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